Methods for Detecting Coincident Sample Events, and Devices and Systems Related Thereto

ABSTRACT

In some aspects of the present disclosure, methods of detecting coincident sample events are provided. The methods include receiving a first set of signal data representing detected signals from a flow cytometer system; detecting, with a peak detection module, one or more peaks within the signal data; and cancelling, with a successive cancellation module, one or more individual sample events from the signal data at corresponding time indexes, wherein the cancellation of more than one individual sample event is successive. Devices and system related thereto are also provided.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional PatentApplication No. 61/785,301 filed Mar. 14, 2013, which application isincorporated herein by reference in its entirety.

SUMMARY

In some aspects of the present disclosure, methods of detectingcoincident sample events are provided. The methods include receiving afirst set of signal data representing detected signals from a flowcytometer system; detecting, with a peak detection module, one or morepeaks within the signal data; and cancelling, with a successivecancellation module, one or more individual sample events from thesignal data at corresponding time indexes, wherein the cancellation ofmore than one individual sample event is successive.

In some aspects of the present disclosure, flow cytometer systems areprovided. The flow cytometer systems include a flow cell for streaming ahydro-dynamically focused core stream past an interrogation zone; beamshaping optics positioned to receive and manipulate a first light beam,and to produce a resulting light beam that irradiates the core stream atthe interrogation zone of the flow cell; a detection system to detectresulting light from the flow cell when irradiated; and a dataprocessing system operably coupled to the detection system to generateand process signal data representing the resulting light detected by thedetection system. The processing of the signal data includes receiving afirst set of signal data from the detection system; detecting one ormore peaks within the signal data; and cancelling one or more individualsample events from the signal data at corresponding time indexes,wherein the cancellation of more than one individual sample event issuccessive.

In some aspects of the present disclosure, non-transientmachine-readable mediums are provided. The machine readable mediums havemachine-executable instructions stored thereon, which when executed byone or more processing devices, cause the one or more processing devicesto receive a first set of signal data representing detected signals froma flow cytometer system; detect, with a peak detection module, one ormore peaks within the signal data; and cancel, with a successivecancellation module, one or more individual sample events from thesignal data at corresponding time indexes, wherein the cancellation ofmore than one individual sample event is successive.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings, which are incorporated herein, form part ofthe specification. Together with this written description, the drawingsfurther serve to explain the principles of, and to enable a personskilled in the relevant art(s), to make and use the systems and methodspresented. In the drawings, like reference numbers indicate identical orfunctionally similar elements.

FIG. 1 illustrates a flow cytometer system, according to one embodiment.

FIG. 2 illustrates a data processing system, according to oneembodiment.

FIG. 3 illustrates a flow chart for detecting coincident sample events,according to one embodiment.

FIG. 4 illustrates a plot of a combined output response for a coincidentevent, along with plots of the individual sample events within thecoincident event, according to one embodiment.

FIG. 5 illustrates an example plot of skewness, according to oneembodiment.

FIG. 6 illustrates a plot of a combined response for a coincident eventhaving a single peak, along with plots of the individual sample eventswithin the coincident event, according to one embodiment.

FIG. 7 illustrates a plot of a combined response for a coincident event,according to one embodiment;

FIG. 8 illustrates a functional block diagram of a base line restorationmodule, peak detection module, successive cancellation module, andchannel analysis module which perform the method of FIG. 3, according toone embodiment

DETAILED DESCRIPTION

Before the embodiments of the present disclosure are described, it is tobe understood that the present disclosure is not limited to particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the embodiments of the present disclosurewill be limited only by the appended claims.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentinvention. Any recited method can be carried out in the order of eventsrecited or in any other order which is logically possible.

In some aspects, the methods, devices, and systems of the presentdisclosure relate to sample detection in flow cytometers. The specificsample may vary depending on application and may include, but is notlimited to, cells, particles, or combinations thereof. For example, incertain embodiments, the sample may include one or more of thefollowing: red blood cells (RBC), white blood cells (WBC), and platelets(PLC). The systems and methods may relate to various types of processingof the samples, such as identification of the samples, differentiationof the samples, counting of the sample, etc.

The term “event” is used herein to refer generally to one or moresamples (e.g., cells and/or particles) passing through the interrogationzone of a flow cell and being irradiated by a light source (e.g., laserbeam). The event generates a resulting light signal that is detected bya detection system. The resulting light signal may include manydifferent detected parameters such as, but not limited to, axial lightloss, scattered light, fluorescence, etc. All of the parameters make upwhat is referred to herein as the “profile of the event”.

The term “signal event”, “incoming signal event”, “signal data” are usedherein to refer generally to the resulting energy detected when one ormore samples pass through the interrogation zone of a flow cell beingirradiated by a light source (e.g., laser beam). The signal event mayinclude one or more sample events. The term “sample event” is usedherein to refer generally to the resulting energy of a single samplepassing through the interrogation zone and producing parameters thatmake up the “sample event profile” for that sample event.

In some instances, two or more sample may pass through the interrogationzone of the flow cell while being proximate to one another. The term“coincident event” and “coincident sample event” are used herein torefer generally to the occurrence of two samples (e.g., cells,particles, etc.) passing through the interrogation zone while beingproximate to one another. The resulting “event profile” for theresulting signal will have overlapping “sample event profiles”.

In some aspects, the methods, devices, and systems of the presentdisclosure relate to detecting sample events, and identifying andresolving coincident sample events, such as those occurring within aflow cell of a hematology analyzer for example.

In some aspects, the methods, devices, and systems detect precise peakheights and sample event profiles (e.g., area under the pulse, pulsewidth, and higher order moments) during coincidence sample events. Thesystem and methods provided herein enable a more precise detection ofcoincident sample events, such as sample events that are proximate toone another and/or smaller sample events that are close to larger sampleevents. By computing precise peak heights and sample event profileswithin the coincidence event, assays may be run with higherconcentrations (i.e., less diluted). For instance, certain assays in ananalyzer may need to be highly diluted to find the correct count of eachcell and/or particle as well as ratio between the two counts—e.g., aRetic assay to find the percentage ratio between RBC and Reticulocyte.Capturing sample events at a higher resolution permits more coincidentevents to pass through the flow cell, and enables assays to run forshorter times while still maintaining high statistical accuracy andhigher precision of the sample event parameters, which effectivelyincreases throughput. Samples can be processed at a faster rate—e.g.,1.5 to 2 times faster, and greater. The systems and methods describedherein enable precision with higher concentrated sample levels than withthose resulting from statistical correction. Furthermore, the higherconcentration samples use less sheath consumption and less waste.

As summarized above, in some aspects of the present disclosure, methodsof detecting coincident sample events are provided. The methods includereceiving a first set of signal data representing detected signals froma flow cytometer system; detecting, with a peak detection module, one ormore peaks within the signal data; and cancelling, with a successivecancellation module, one or more individual sample events from thesignal data at corresponding time indexes, wherein the cancellation ofmore than one individual sample event is successive.

As summarized above, in some aspects of the present disclosure, flowcytometer systems are provided. The flow cytometer systems include aflow cell for streaming a hydro-dynamically focused core stream past aninterrogation zone; beam shaping optics positioned to receive andmanipulate a first light beam, and to produce a resulting light beamthat irradiates the core stream at the interrogation zone of the flowcell; a detection system to detect resulting light from the flow cellwhen irradiated; and a data processing system operably coupled to thedetection system to generate and process signal data representing theresulting light detected by the detection system. The processing of thesignal data includes receiving a first set of signal data from thedetection system; detecting one or more peaks within the signal data;and cancelling one or more individual sample events from the signal dataat corresponding time indexes, wherein the cancellation of more than oneindividual sample event is successive.

As summarized above, in some aspects of the present disclosure,non-transient machine-readable mediums are provided. The machinereadable mediums have machine-executable instructions stored thereon,which when executed by one or more processing devices, cause the one ormore processing devices to receive a first set of signal datarepresenting detected signals from a flow cytometer system; detect, witha peak detection module, one or more peaks within the signal data; andcancel, with a successive cancellation module, one or more individualsample events from the signal data at corresponding time indexes,wherein the cancellation of more than one individual sample event issuccessive.

The following detailed description of the figures refers to theaccompanying drawings that illustrate exemplary embodiments. Otherembodiments are possible. Modifications may be made to the embodimentsdescribed herein without departing from the spirit and scope of thepresent invention. Therefore, the following detailed description is notmeant to be limiting.

FIG. 1 illustrates a top view of a flow cytometer system, according toone embodiment. Flow cytometer system 100 is shown including beamshaping optics 110, flow cell 120, light source 140, detection system130, and data processing system 140.

Light source 140 may include, for example, a laser coupled to an opticalfiber to generate a laser beam directed to the beam shaping optics 110positioned between the light source 140 and flow cell 120. The laserbeam is manipulated by the beam shaping optics 110 to provide a focusedbeam directed to an interrogation zone of the flow cell 120. A corestream within the flow cell 120 is irradiated by the focused beam as itflows past the interrogation zone of the flow cell 120.

Flow cell 120 is positioned such that the light from the beam shapingoptics 110 is directed to an interrogation zone in the flow cell 120.Flow cell 120 includes a core stream which is directed past theinterrogation zone of the flow cell 120. In this way, the core streamflowing through the flow cell 120 is irradiated by the focused light asit passes through the interrogation zone. The core stream may include,for example, samples (e.g., particles, cells, or combinations thereof)which are hydro-dynamically focused in a fluid sheath (e.g., injectedinto the center of the fluid sheath) and directed past the interrogationzone in the flow cell 102.

Detection system 130 is positioned next to the flow cell to detect lightemitted from the flow cell. As samples pass through the interrogationzone, the resulting light characteristics, such as light scatter, lightloss, fluorescence, etc. For example, detection system 130 may include aphotomultiplier tube (PMT), photodiode (PD), etc., for detecting lightand converting it to an electrical signal. Detection system 130 mayinclude one or more detectors to detect axial light loss, and/or one ormore detectors to measure the amount of scattered light resulting whenthe core stream is irradiated at the interrogation zone. For instance,the detection system 130 may include one or more detectors to detectintermediate angle scatter (IAS) and/or forward scatter. The detectionsystem 130 may also include lenses and detectors for detectingfluorescent light, polarized side scatter, and/or depolarized sidescatter. Furthermore, one or more detectors may be positioned in variouspositions around the flow cell—e.g., at 90 degrees from the flow cell.The detection system 130 may also include other components such aslenses, reflectors or mirrors, etc., which are not shown. For example,detection system 130 may include components such as lenses, reflectorsor mirrors, etc.

The light characteristics, resulting from each interrogated sample(e.g., particle or cell), is detected to generate correspondingelectrical signals. Data processing system 140 is operably coupled todetection system 130 and receives the corresponding electrical signals.These electrical signals are converted from analog signals to digitalsignals by an analog-to-digital converter (ADC), for example to generatesignal data at a given sampling rate to represent the electrical signalsfrom the detection system 130. In certain embodiments, the analogelectrical signals may go through a pre-amplification stage before beingconverted to digital signals. The term “signal data” is used herein torefer generally to the digital signal generated from sampling the analogsignal.

Data processing system 140 may use or otherwise process the signal datato determine various parameters of the sample events (e.g., for samplecells and/or particles). Example sample event parameters that aredetermined by data processing system 140 may include, but are notlimited to, magnitudes of the signal (e.g., signal pulse from a detectedevent), signal peaks and their respective heights, signal widths, areasunder the signal. In certain embodiments, data processing system maydetermine one or more parameters of higher order moments—e.g., standarddeviation of the signal (e.g., second order moment), skewness of thesignal (e.g., third order moment), and kurtosis of the signal (e.g.,fourth order moment). Other parameters may also be found, such as theDiscrete Fourier Transform (DFT). These parameters may then be used forfurther analysis, such as for cell classification purposes. The dataprocessing system 140 uses one or more of these parameters to identifyand resolve coincident events occurring within the flow cell.

FIG. 2 illustrates an example block diagram of a data processing systemupon which the disclosed embodiments may be implemented. Embodiments ofthe present invention may be practiced with various computer systemconfigurations such as hand-held devices, microprocessor systems,microprocessor-based or programmable user electronics, minicomputers,mainframe computers and the like. The embodiments can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a wire-based orwireless network. FIG. 2 shows one example of a data processing system,such as data processing system 200, which may be used with the presentdescribed embodiments. Note that while FIG. 2 illustrates variouscomponents of a data processing system, it is not intended to representany particular architecture or manner of interconnecting the componentsas such details are not germane to the techniques described herein. Itwill also be appreciated that network computers and other dataprocessing systems which have fewer components or perhaps morecomponents may also be used. The data processing system of FIG. 2 may,for example, be a personal computer (PC), workstation, tablet,smartphone or other hand-held wireless device, or any device havingsimilar functionality. Furthermore, the term “data processing system”may also encompass programmable circuitry programmed or configured bysoftware and/or firmware, or within special-purpose “hardwired”circuitry, or a combination of such forms. Such special-purposecircuitry (if any) can be in the form of, for example, one or moreapplication-specific integrated circuits (ASICS), programmable logicdevices (PLDs), field-programmable gate arrays (FPGAs), etc. Forexample, the data processing device may be in the form of a FPGAincluding a various modules operably and communicably coupled to oneanother. For instance, the FPGA may include a module functioning as aprocessing device, a module functioning as memory, a base linerestoration module, a peak detection module, a successive cancellationmodule, a channel analysis module, etc.

For the example embodiment shown in FIG. 2, the data processing system201 includes a system bus 202 which is coupled to a microprocessor 203,a Read-Only Memory (ROM) 207, a volatile Random Access Memory (RAM) 205,as well as other nonvolatile memory 206. In the illustrated embodiment,microprocessor 203 is coupled to cache memory 204. System bus 202 can beadapted to interconnect these various components together and alsointerconnect components 203, 207, 205, and 206 to a display controllerand display device 208, and to peripheral devices such as input/output(“I/O”) devices 210. Types of I/O devices can include keyboards, modems,network interfaces, printers, scanners, video cameras, or other deviceswell known in the art. In one embodiment, I/O device includes interfacefor receiving data derived from the detection system 130. In someinstances, the signal data received is already converted to a digitalsignal, and in other instances, the interface includes an analog todigital converter to digitize the incoming signal into the signal datato be processed. I/O devices 210 may in some instances be coupled to thesystem bus 202 through I/O controllers 209. In one embodiment, the I/Ocontroller 209 includes a Universal Serial Bus (“USB”) adapter forcontrolling USB peripherals or other type of bus adapter.

RAM 205 can be implemented as dynamic RAM (“DRAM”) which requires powercontinually in order to refresh or maintain the data in the memory. Theother nonvolatile memory 206 can be a magnetic hard drive, magneticoptical drive, optical drive, DVD RAM, or other type of memory systemthat maintains data after power is removed from the system. While FIG. 2shows that nonvolatile memory 206 as a local device coupled with therest of the components in the data processing system, it will beappreciated by skilled artisans that the described techniques may use anonvolatile memory remote from the system, such as a network storagedevice coupled with the data processing system through a networkinterface such as a modem or Ethernet interface (not shown).

Data processing system 140 receives the detected signal from thedetection system 130. If the detected signal from the detection system130 has not be digitized yet, the data processing system 140 digitizesthe detected signal—e.g., with an analog-to-digital converter (ADC)—togenerate the signal data for processing. The sampling rate of theanalog-to-digital converter may vary in different embodiments. In oneembodiment, the analog-to-digital converter of the data processingsystem digitizes the incoming signal from the detection system (e.g., aphotomultiplier tube (PMT), photodiode (PD), etc.) at 10 million samplesper second (MSPS).

For example, each sample passing through the flow cell interacts withthe laser beam and produces a convolution output of a laser beam profileand sample size in the flow-direction. This convolution signal iscaptured by the detection system (e.g., PMT or PD) and digitized by theADC. This digitized output is the input signal data for the methodsdescribed herein.

The methods may include processing the signal data to find the peakheight of the event as well as detecting possible individual sampleevents within the coincident events. Other parameters may also bedetermined, such as the pulse width, the area under curve, and otherhigher order moments which may be computed once the pulse shape of theindividual cell profile is extracted from the coincidence event. Themethods may also find the peaks of the event even under the noisy signalcondition, preventing false detections from electronic or interferencenoise.

Once a peak of a sample event is detected in the signal data, a modelsample event profile is generated from a pre-stored beam profile and thedetected peak height of that sample event. If more than one peak heightsare detected during the detection process, the energy of the individualsample events are computed based on their peak heights, and the energyof the sample event are subtracted successively from the combined eventresponse at the corresponding time indexes of the peaks. In oneembodiment, the signal with maximum peak value is subtracted first fromcombined (coincident) signal response. The algorithm works successivelyon leftover signal energy to detect any other smaller sample eventswithin the coincident event until the energy of the leftover signal doesnot decrease by the amount of the subtracted signal energy.

In one embodiment, the successive cancellation process is also iterativein that it corrects a peak height determination of a previously detectedand cancelled sample event. For example, when sample events are closetogether, the first larger peak height may first be detected andcancelled from the signal data. Next, the second peak is detected forcancellation. The second peak height is detected and the second peakcancelled from the original signal data. Based on the cancellation ofthe second peak from the original signal data, the first peak height maythen be corrected. The successive cancellation process may continue withthe second peak being successively cancelled from the remaining signaldata after the cancellation of the first peak.

When only a single peak event is detected, skewness (3^(rd) ordermoment) may be computed to detect whether there any small sample eventsriding at the leading or lagging trail of a larger sample event—e.g., asshown in FIGS. 5 and 6. For example, when a Skewness of more than apredetermined threshold (e.g., +/−0.15) is computed, a successivecancellation is initiated to obtain the correct sample event heights andsample event profiles. In one embodiment, the successive cancellation isalso iterative as similarly described above.

FIG. 3 illustrates a flowchart for a method of detecting coincidentsample events, according to one embodiment. Method 300 begins byperforming a base line restoration in order to determine the preciseheight of a sample event (Xp). For example, a base line restorationmodule may receive a set of signal data and perform a base linerestoration. The base line restoration provides for a more preciseestimation by accounting for system variations and fluctuations, such asthose caused by laser optics, electronics, noise, interference,temperature variations, thermal radiations, fluid viscosity, etc.

At block 302, a new set of signal data is obtained—e.g., from ananalog-to-digital converter (ADC) coupled to a detection system of aflow cytometer system. A base line restoration value is computed bytaking the median value of a previous set of signal data that fall undera predetermined threshold value, T, as represented by blocks 304, 306,and 308. For example, in block 306, the median value is calculated froma 15 microsecond time period of signal data that is under thepredetermined threshold, T. For instance, the 15 microsecond time periodprovides approximately 150 samples at the 10 MHz sampling rate, forinstance. It should be appreciated that the time period and samplingrate may vary in different embodiments. The computed median value isused as the base line restoration value, or is used as the updated baseline restoration value, as represented by block 308.

The predetermined threshold value represents a threshold value for theindication of one or more sample events has occurred. The predeterminedthreshold value may be experimentally defined and may vary for differentparticles or cells being implemented. The threshold for red blood cells(RBC) may be implemented as, but is not limited to, 1.25% of the fulldynamic range, for example. The threshold for white blood cells (WBC)may be implemented as, but is not limited to, 6-7% of the full dynamicrange, for example. The threshold for Reticulocyte may be implementedas, but is not limited to, 2-3% of the full dynamic range, for example.These example values are exemplary and should not be construed aslimiting.

At block 310, the base line restoration is performed by subtracting theupdated base line restoration value from block 308 (e.g., the computedmedian value from block 306) from the incoming signal data, X(i), whenthe signal data goes above the predetermined threshold value, T, inblock 302 and 304. In the embodiment shown, the sampling rate is 10million samples per second (MSPS). At block 312, the resulting signaldata after subtracting the base line restoration value is then used forfurther processing, such as for peak detection and/or coincidencecorrection. In some instances, the resulting signal data may be bufferedor stored in memory.

Once the base line restoration is performed, a peak detection moduledetects the peak of the signal event. At block 314, the peak detectionmodule determines whether sample events are detected. In one embodiment,a signal event, which may include one or more sample events, is detectedwhen a peak of the signal pulse increases above the predeterminedthreshold value. There may also be more than one sample event, as wellas more than one peak above the threshold value. Accordingly, the peakdetection module identifies peaks of the signal pulse and detectswhether they are above the predetermined threshold. In one embodiment,for example, the signal event detection criteria may be as follows:

-   -   X_(p)≧T    -   X_(p)≧X_(p+1)≧T X_(p)≧X_(p+2)≧T X_(p)≧X_(p+3)≧T    -   X_(p)≧X_(p−1)≧T X_(p)≧X_(p−2)≧T X_(p)≧X_(p−3)≧T    -   X_(p)≧X_(p+4) X_(p)≧X_(p+5) X_(p)≧X_(p+6)    -   X_(p) X_(p−4) X_(p) X_(p−5) X_(p) X_(p−6)

where T is a predetermined or programmable threshold value, and X_(p+n)is the n^(th) sample from X_(p). If the incoming signal data satisfiesall of these conditions, then single or multiple peak heights of thesignal event may be recorded.

At block 316, the peak detection module determines whether more than onepeak is detected within the signal event. If multiple peak heights aredetected within the signal event, then it is determined that multiplesample events are closely detected during the time when samples wereabove the threshold value. A successive cancellation module may then beexecuted to successively cancel out the energy of individual sampleevents, as represented by the “Yes” arrow from block 316 to block320—e.g., to successively subtract out the energy of one sample event ata time. In one embodiment, the cancellation process may also beiterative in that one or more previous peak height determinations fromprevious cancellations may be corrected based on the cancellation of asuccessive peak from the original signal data.

In order to measure the precise peak height of each individual sampleevent within the coincidence, the successive cancellation modulesubtracts the energy for each sample event individually from the signaldata (e.g., the combined input signal for the multiple sample events).In one embodiment, the largest sample events are detected and subtractedfirst one at a time, to permit clearer detection of smaller sampleevents within the signal data. For example, white blood sample eventsmay be larger than red blood sample events and platelet events, in whichcase the white blood sample events would be detected and subtracted outsuccessively first. The detection of the precise peak of the pulseenables the successful detection and cancellation of the stronger signal(e.g., WBC without introducing any residual energy that may cause anerror e.g., by being mistaken as a small sample event. Again, thecancellation process may also be iterative in that one or more previouspeak height determinations from previous cancellations may be correctedbased on the cancellation of a successive peak from the original signaldata.

At block 318, the largest peak sample event and the corresponding timeindex for the sample event is identified. From the peak information, anideal waveform of the Gaussian signal may be generated—e.g., based on astored or predetermined beam profile and the height of the maximum peakfound—as represented by block 320.

In one embodiment, the combined input signal (e.g., signal data) ismathematically represented by the sum of convolutions of the laser beamprofile and the size of the sample in flow direction, such asrepresented by the following equation.

${x(t)} = {{\sum\limits_{i = 1}^{N}\; {X_{p}*{h(t)}}} + {n(t)}}$

where x(t) is the incoming set of signal data; X_(p) size of theindividual sample events in the flow-direction within the incoming setof signal data; h(t) is the laser bean profile; * is the convolutionprocess; and n(t) is the electronics and/or interference noise.Therefore, once the size of the sample event in the flow-direction,X_(p), is detected, a model sample event, representing an idealconvoluted signal, may be generated from the saved laser beam profile.For instance, in one embodiment, the model sample event (e.g., idealconvoluted signal for that sample event) may be found from the followingequation.

x′ _(p) =X _(p) *h(t)

The energy from the model sample event (e.g., ideal convoluted signal),x′_(p), is then subtracted at the correct time index from the energy ofthe signal data, x(t), as represented block 322. An example equation ofthe energy of the signal data may be represented as follows.

x(t)=x(t)−x′ _(p)

At block 324, a determination is made as to whether the total energy ofthe signal data is reduced by the generated energy of the model cell(e.g., the ideal convoluted sample signal). In other words, whetherthere is remaining energy left when the energy of the model sample eventis subtracted from the energy of the signal data.

If significant leftover energy remains (e.g., the energy of the signaldata is reduced by energy of the model sample event), then it is inputagain for the next successive cancellation to find anotherequivalently-sized sample event or smaller sample event within thesignal data (e.g., combined signal), as represented by the arrow formblock 324 back to block 318. For example, at block 318, the largest peaksample event is then detected for the remaining signal data left overfrom the first cancellation. At block 320, a second model sample eventis generated for this peak sample event, and at block 322, the energyfrom the second model sample event is successively subtracted at thecorresponding time index from the remaining sample data left over fromthe first cancellation. Thereafter, at block 324, a determination ismade as to whether the total energy of the signal data is reduced afterthe cancellation of the first and second cancellations (e.g., whetherthere is any leftover energy after the first and second cancellation).In one embodiment where the cancellation is iterative, the secondgenerated model sample event is successively subtracted at thecorresponding time index from the original signal data, and based on theresults of this subtraction, the first peak height (determined for thefirst sample event that was cancelled) is corrected.

It should be appreciated that the iterative correction may occur for aprevious peak height that is not necessarily the immediately precedingpeak height that was determined and cancelled. For example, after thecancellation of two peaks, a third peak detected for cancellation may beused to generate a third model sample event for the third peak, and theenergy for the third model sample event may be subtracted from theoriginal signal data to correct the peak height determination of thefirst peak and/or second peak height.

If the energy does not decrease (e.g., if there is no remaining leftoverenergy or insignificant or negligible leftover energy), for example asrepresented by the following equation, then it may be determined thatall the sample events within that coincident event were detected.

x(t)−x′ _(p) ≧x′ _(p)

If no leftover energy remains (the total energy of the signal is notreduced), then all the individual sample events within the incomingsignal are detected. Once the individual peaks for the incoming signalhave been identified with right time index, then it is possible torepeat the same calculations for all the channels. Each channel mayrepresent, for example, detection of various data, such as, but notlimited to, side scatter, axial light loss, polarized scatter,depolarized scatter, fluorescence, etc. However, because the individualsample events occur at the same time, the time indexes of eachindividual peak may be used to find the corresponding features at thosetimes in the other channels. In some instances, a range of time based onthe time indexes may be used, such as within a predetermined time beforeand after the time indexes. This provides an alternative way to obtainthe same information from the other channels, rather than performing thesame process on the other channels, which may consume more processingpower and time. For example, as shown in FIG. 3, the time indexes foreach of the individual sample event peaks may be used to find thecorresponding features in other channels at those approximate times thatthe sample events occur. For example, at block 330, a channel analysismodule identifies the corresponding features in the other channels(e.g., sample event peaks), by identifying a time range based on thetime indexes of the individual sample events detected by the successivecancellation module. For instances, in the embodiment shown, the timerange is +/−0.5 microseconds from the time indexes of the individualsample events. Other tolerances, or sizes of the time range, may vary indifferent embodiments. In this way, sample event profile data from allchannels may be gathered for each sample event, as represented by block132.

FIG. 4 illustrates a plot of an example set of signal data with acombined output signal for multiple sample events that are proximate toone another, according to one embodiment. The plot 400 illustrates thesignal data 405 (e.g., combined output signal for multiple sampleevents) that is received from the analog to digital converter and whichincludes multiple peaks. For example, the horizontal axis shows samplestaken at a sampling rate of 10 MHz. The vertical axis shows theassociated voltage level for each sample. As shown, the combined outputsignal 405 includes a triplet of sample events 410, 415, and 420. Theresulting combined output signal 405 is shown having three peaks 425,430, and 435, which are produced from the occurrence of sample events,410, 415, and 420. The successive cancellation module described for FIG.3, for example, extracts the energy for these sample events successivelyand iteratively by cancelling the energy of the strongest signal first.

Returning to FIG. 3, if at block 316, only one peak height is foundwithin the sample event, then the skewness of the pulse is computed, asrepresented by block 326. Skewness is a third order moment of the pulse,and may be used to determine whether a smaller sample event is presentproximate to a larger sample event. In one embodiment, for example,skewness may be computed by the following equation.

${Skew} = {\frac{n}{\left( {n - 1} \right)\left( {n - 2} \right)}{\sum\left( \frac{x_{i} - \overset{\_}{x}}{s} \right)^{2}}}$

A predetermined skewness threshold may be predetermined and used toindicate whether a smaller sample event is proximate a larger sampleevent. For example, if the computed skewness is greater than thepredetermined threshold level (e.g., +/−0.15), then it may be determinedthat a smaller cell even is proximate a larger sample event, and thenthe successive cancellation module may be executed to subtract out thelargest sample event, as represented by the arrow from block 328 toblock 318. If the computed skewness is less than the predeterminedthreshold level, then it is determined that no smaller sample event ispresent, and to block 330 where the local maximum of the peak amplitudewithin a range (e.g., +/−0.5 microseconds) is identified for otherchannels of data for all the peaks found in the window. In this way,sample event data from all channels may be gathered for the sampleevent, as represented by block 332.

FIG. 5 illustrates an example of a positively skewed distribution 510and a negatively skewed distribution 520. A positive or negative valueof skewness indicates that a pulse has either leading or lagging trail.Leading and lagging trail is caused by any smaller cells that pass invery close proximity of a larger cell. Once the larger sample eventresponse is subtracted as per the successive cancellation algorithmdescribed above from the combined response, precise peak height as wellas higher order moments can be computed for the smaller sample event.

FIG. 6 illustrates an example combined output of a large sample event inclose proximity to a smaller sample event. As show, plot 600 illustratessignal data 610 (e.g., combined output signal for the multiple sampleevents) that is received from the analog to digital converter. Forexample, the horizontal axis shows samples taken at a sampling rate of10 MHz. The vertical axis shows the associated voltage level for eachsample. As shown, the combined output signal 610 includes a large sampleevent 615 (e.g., from a red blood cell, RBC) and a smaller sample event620 (e.g., from a smaller platelet, PLT). The resulting combined outputsignal 610 is a larger event that is skewed on the lagging trail (on theright). Furthermore, once the stronger signal (i.e., the larger sampleevent) is cancelled at the right offset (e.g., time index), the weakersignal (i.e., the smaller sample event) is easily detectable.

FIG. 7 illustrates an example combined output signal 710 when a smallersample event (e.g., platelet) is between two large sample events (e.g.,red blood cells) and at the shoulder of the first large sample event.The large peaks 715 and 725 are indicative of the larger sample events(e.g., due to the red blood cells), and the skewness seen at 720 isindicative that a small sample event (e.g., platelet) is present.

FIG. 8 illustrates a functional block diagram of a base line restorationmodule, peak detection module, successive cancellation module, andchannel analysis module which perform the method shown in FIG. 3,according to one embodiment. The base line restoration module 820, peakdetection module 830, successive cancellation module 840, and channelanalysis module 850 are shown within memory 810 and may be executed by aprocessing device (e.g., processor, microprocessor, application-specificintegrated circuits (ASICS), programmable logic devices (PLDs),field-programmable gate arrays (FPGAs), etc.) to perform the functionsas identified in the method described in FIG. 3. Memory 810 may include,for example, embedded or non-embedded memory, as well as nonvolatile orvolatile memory. For instance, memory 801 may include memory 206,volatile RAM 205, ROM 207, such as shown in FIG. 2; or may be embeddedin an ASIC or FPGA, etc.; or may also include, but is not limited to,non-transient machine readable mediums, such as described further below.The term processing device is used broadly herein, and may refer to oneor more processor, microprocessors, application-specific integratedcircuits (ASICS), programmable logic devices (PLDs), field-programmablegate arrays (FPGAs), etc., and/or any other processing device.

Other embodiments and modifications within the scope of the presentdisclosure will be apparent to those skilled in the relevant art.Various modifications, processes, as well as numerous structures towhich the embodiments of the present disclosure may be applicable willbe readily apparent to those of skill in the art to which the presentdisclosure is directed upon review of the specification. Various aspectsand features of the present disclosure may have been explained ordescribed in relation to understandings, beliefs, theories, underlyingassumptions, and/or working or prophetic examples, although it will beunderstood that the present disclosure is not bound to any particularunderstanding, belief, theory, underlying assumption, and/or working orprophetic example.

It should be understood that some of the techniques introduced above canbe implemented by programmable circuitry programmed or configured bysoftware and/or firmware, or they can be implemented entirely byspecial-purpose “hardwired” circuitry, or in a combination of suchforms. Such special-purpose circuitry (if any) can be in the form of,for example, one or more application-specific integrated circuits(ASICS), programmable logic devices (PLDs), field-programmable gatearrays (FPGAs), etc.

Software or firmware implementing the techniques introduced herein maybe stored on a machine-readable storage medium and may be executed byone or more general-purpose or special-purpose programmablemicroprocessors. A “machine-readable medium”, as the term is usedherein, includes any mechanism that can store information in a formaccessible by a machine (a machine may be, for example, a computer,network device, cellular phone, personal digital assistant (PDA),manufacturing took, any device with one or more processors, etc.). Forexample, a machine-accessible medium includes recordable/non-recordablemedia (e.g., read-only memory (ROM); random access memory (RAM);magnetic disk storage media; optical storage media; flash memorydevices; etc.), etc.

The preceding examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how tomake and use the embodiments of the invention, and are not intended tolimit the scope of what the inventors regard as their invention nor arethey intended to represent that the experiments below are all or theonly experiments performed. Efforts have been made to ensure accuracywith respect to numbers used (e.g., amounts, temperature, etc.) but someexperimental errors and deviations should be accounted for. Unlessindicated otherwise, parts are parts by weight, molecular weight isweight average molecular weight, temperature is in degrees Centigrade,and pressure is at or near atmospheric.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimits of that range is also specifically disclosed. Each smaller rangebetween any stated value or intervening value in a stated range and anyother stated or intervening value in that stated range is encompassedwithin the present disclosure. The upper and lower limits of thesesmaller ranges may independently be included or excluded in the range,and each range where either, neither or both limits are included in thesmaller ranges is also encompassed within the present disclosure,subject to any specifically excluded limit in the stated range. Wherethe stated range includes one or both of the limits, ranges excludingeither or both of those included limits are also included in the presentdisclosure.

In the description of the present disclosure herein, it will beunderstood that a word appearing in the singular encompasses its pluralcounterpart, and a word appearing in the plural encompasses its singularcounterpart, unless implicitly or explicitly understood or statedotherwise. Further, it will be understood that for any given componentdescribed herein, any of the possible candidates or alternatives listedfor that component, may generally be used individually or in combinationwith one another, unless implicitly or explicitly understood or statedotherwise. Additionally, it will be understood that any list of suchcandidates or alternatives, is merely illustrative, not limiting, unlessimplicitly or explicitly understood or stated otherwise.

Various terms are described below to facilitate an understanding of thepresent disclosure. It will be understood that a correspondingdescription of these various terms applies to corresponding linguisticor grammatical variations or forms of these various terms. It will alsobe understood that the present disclosure is not limited to theterminology used herein, or the descriptions thereof, for thedescription of particular embodiments. The publications discussed hereinare provided solely for their disclosure prior to the filing date of theapplication. Nothing herein is to be construed as an admission that theembodiments of the present disclosure are not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dateswhich may need to be independently confirmed.

1-37. (canceled)
 38. A data processing system, comprising: a processor;memory operably coupled to the processor, wherein the memory includesexecutable instructions stored thereon, which when executed by theprocessor, cause the processor to: receive a first set of signal data;detect one or more peaks within the signal data; and cancel one or moreindividual sample events from the signal data.
 39. The data processingsystem of claim 38, wherein the first set of signal data representdetected signals from a flow cytometer system.
 40. The data processingsystem of claim 38, wherein the one or more peaks within the signal datais detected with a peak detection module.
 41. The data processing systemof claim 38, wherein the one or more individual sample events arecancelled with a successive cancellation module.
 42. The data processingsystem of claim 38, wherein the data processing system comprisesprogrammable circuitry.
 43. The data processing system of claim 42,wherein the programmable circuitry is configured by: (a) software; (b)firmware; (c) special-purposed hardwired circuitry; or a combination of(a)-(c).
 44. The data processing system of claim 43, wherein thespecial-purposed hardwired circuitry is in the form of one or moreapplication-specific integrated circuits (ASICS), programmable logicdevices (PLDs), or field-programmable gate arrays (FPGAs).
 45. The dataprocessing system of claim 44, wherein the FPGA comprises: a modulefunctioning as a processing device; a module functioning as memory; abase line restoration module; a peak detection module; a successivecancellation module; and a channel analysis module.
 46. The dataprocessing system of claim 38, wherein one or more individual sampleevents from the signal data are cancelled at corresponding time indexes.47. The data processing system of claim 38, wherein the cancellation ofmore than one individual sample event is successive.
 48. The dataprocessing system of claim 38, wherein the data processing system isoperably coupled to a detection system of the flow cytometer system. 49.The data processing system of claim 48, wherein the detection systemcomprises a photomultiplier tube (PMT) or a photodiode (PD) to generateand process the signal data.
 50. The data processing system of claim 49,wherein the detection system detects resulting light from a flow cell ofthe flow cytometer system.
 51. The data processing system of claim 38,wherein the cancellation comprises: detecting a first peak event and acorresponding first time index for the first peak event; generating amodel sample event based on a stored beam profile and height of thefirst peak event; and subtracting out energy for the model sample eventfrom energy of the signal data at the corresponding first time index.52. The data processing system of claim 51, wherein multiple peaks aredetected within the signal data, and wherein when the energy of thesignal data is decreased by the energy of the model sample event, thecancellation steps of claim 51 are successively repeated for successivepeak events and corresponding time indexes.
 53. The data processingsystem of claim 38, wherein energy for larger sample events are firstsubtracted out of energy of the signal data.
 54. The data processingsystem of claim 38, wherein the successive cancellation of more than oneindividual sample event is also iterative, wherein the iterativecancellation corrects a peak determination of a previously detected andcancelled sample event.
 55. The data processing system of claim 38,wherein only one peak is detected within the signal data, and whereinthe execution of the instructions by the processor, further causes theprocessor to: compute a skewness of the peak of the signal data todetermine if a smaller sample event is proximate a larger sample event.56. The data processing system of claim 55, wherein the execution of theinstructions by the processor, further causes the processor to: detect askewness indicating that a smaller sample event is proximate a largersample event; wherein a model sample event represents the larger sampleevent, and wherein an energy of the larger sample event is subtractedfrom an energy of the signal data at a corresponding first time index;and wherein a peak height and higher order moments for the smallersample event are computed after subtraction of an energy of the modelsample event from the energy of the signal data at the correspondingfirst time index.
 57. The data processing system of claim 38, whereinthe execution of the instructions by the processor, further causes theprocessor to: perform, with a base line restoration module, a base linerestoration for the received signal data before detecting the one ormore peaks, wherein the base line restoration includes: computing a baseline restoration value based on a median value of a prior set of signaldata; and subtracting the base line restoration value from the receivedsignal data when the received signal data is greater than apredetermined threshold value.
 58. The data processing system of claim38, wherein the execution of the instructions by the processor, furthercauses the processor to: identify characteristics from one or more otherchannels of signal data within a predetermined window of time for eachof the one or more peaks detected.
 59. The data processing system ofclaim 38, wherein the data processing system comprises programmablecircuitry, and the processor and memory are modules embedded within theprogrammable circuitry.